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It consists of clustering words, which are semantically similar and can thus bear a specific meaning. Lin’s algorithm [5] is a prototypical example of word clustering, which is based on syntactic dependency statistics, which occur in a corpus to produce sets of words for each discovered sense of a target word. [6]
In linguistics, a word sense is one of the meanings of a word. For example, a dictionary may have over 50 different senses of the word "play", each of these having a different meaning based on the context of the word's usage in a sentence, as follows: We went to see the play Romeo and Juliet at the theater.
for every sense of the word being disambiguated one should count the number of words that are in both the neighborhood of that word and in the dictionary definition of that sense; the sense that is to be chosen is the sense that has the largest number of this count. A frequently used example illustrating this algorithm is for the context "pine ...
For each context window, MSSA calculates the centroid of each word sense definition by averaging the word vectors of its words in WordNet's glosses (i.e., short defining gloss and one or more usage example) using a pre-trained word-embedding model. These centroids are later used to select the word sense with the highest similarity of a target ...
Senseval-2 – evaluated word sense disambiguation systems on three types of tasks (the all-words, lexical-sample and the translation task) Senseval-3 [usurped] – included tasks for word sense disambiguation, as well as identification of semantic roles, multilingual annotations, logic forms, subcategorization acquisition.
In languages that use inter-word spaces (such as most that use the Latin alphabet, and most programming languages), this approach is fairly straightforward. However, even here there are many edge cases such as contractions , hyphenated words, emoticons , and larger constructs such as URIs (which for some purposes may count as single tokens).
It is used to do hypothesis testing about languages, validating linguistic rules or the frequency distribution of words within languages. Electronically processed corpora provide fast search. Text processing procedures such as tokenization , part-of-speech tagging and word-sense disambiguation enrich corpus texts with detailed linguistic ...
English adjectives, as with other word classes, cannot in general be identified as such by their form, [24] although many of them are formed from nouns or other words by the addition of a suffix, such as -al (habitual), -ful (blissful), -ic (atomic), -ish (impish, youngish), -ous (hazardous), etc.; or from other adjectives using a prefix ...
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